The speech emotion recognition (SER) is the task for recognizing emotion from human speech. The potential applications of SER include human-computer interaction, sentiment analysis and health-care [1]}, [2]}, [3]}, [4]}. Humans naturally sense the emotions in speech while machines find it difficult to characterize them [5]}, [6]}. Techniques proposed till date have significantly increased the machine's ability to recognize speech emotions. However, the task is still challenging mainly due to the presence of large interpersonal and intrapersonal variability and the differences in speech quality used to train and evaluate the system. The goal of this work is to develop an improved SER system by considering emotion-specific acoustic parameters from speech that are assumed to be more robust to unwanted variabilities.
